Overview

Dataset statistics

Number of variables11
Number of observations284807
Missing cells0
Missing cells (%)0.0%
Duplicate rows19335
Duplicate rows (%)6.8%
Total size in memory23.9 MiB
Average record size in memory88.0 B

Variable types

Numeric11

Alerts

Dataset has 19335 (6.8%) duplicate rowsDuplicates
u is highly correlated with g and 6 other fieldsHigh correlation
g is highly correlated with u and 8 other fieldsHigh correlation
r is highly correlated with u and 8 other fieldsHigh correlation
i is highly correlated with u and 7 other fieldsHigh correlation
z is highly correlated with u and 7 other fieldsHigh correlation
uErr is highly correlated with u and 4 other fieldsHigh correlation
gErr is highly correlated with u and 8 other fieldsHigh correlation
rErr is highly correlated with u and 8 other fieldsHigh correlation
iErr is highly correlated with g and 6 other fieldsHigh correlation
zErr is highly correlated with g and 6 other fieldsHigh correlation
u is highly correlated with g and 3 other fieldsHigh correlation
g is highly correlated with u and 3 other fieldsHigh correlation
r is highly correlated with u and 3 other fieldsHigh correlation
i is highly correlated with u and 3 other fieldsHigh correlation
z is highly correlated with u and 3 other fieldsHigh correlation
uErr is highly correlated with gErr and 1 other fieldsHigh correlation
gErr is highly correlated with uErr and 1 other fieldsHigh correlation
rErr is highly correlated with uErr and 1 other fieldsHigh correlation
u is highly correlated with g and 2 other fieldsHigh correlation
g is highly correlated with u and 5 other fieldsHigh correlation
r is highly correlated with g and 6 other fieldsHigh correlation
i is highly correlated with g and 6 other fieldsHigh correlation
z is highly correlated with g and 6 other fieldsHigh correlation
uErr is highly correlated with u and 1 other fieldsHigh correlation
gErr is highly correlated with u and 7 other fieldsHigh correlation
rErr is highly correlated with g and 6 other fieldsHigh correlation
iErr is highly correlated with r and 5 other fieldsHigh correlation
zErr is highly correlated with r and 4 other fieldsHigh correlation
u is highly correlated with g and 6 other fieldsHigh correlation
g is highly correlated with u and 6 other fieldsHigh correlation
r is highly correlated with u and 6 other fieldsHigh correlation
i is highly correlated with u and 3 other fieldsHigh correlation
z is highly correlated with u and 3 other fieldsHigh correlation
uErr is highly correlated with u and 6 other fieldsHigh correlation
gErr is highly correlated with u and 6 other fieldsHigh correlation
rErr is highly correlated with u and 6 other fieldsHigh correlation
iErr is highly correlated with uErr and 3 other fieldsHigh correlation
zErr is highly correlated with uErr and 3 other fieldsHigh correlation
uErr is highly skewed (γ1 = 456.7235232) Skewed
gErr is highly skewed (γ1 = 422.3944341) Skewed
rErr is highly skewed (γ1 = 118.8801239) Skewed
iErr is highly skewed (γ1 = 142.2519051) Skewed
zErr is highly skewed (γ1 = 41.65436631) Skewed

Reproduction

Analysis started2022-02-27 19:48:40.179453
Analysis finished2022-02-27 19:49:08.295438
Duration28.12 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

ID
Real number (ℝ≥0)

Distinct265471
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.237664166 × 1018
Minimum1.23764588 × 1018
Maximum1.237680531 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2022-02-27T16:49:08.344625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.23764588 × 1018
5-th percentile1.23765125 × 1018
Q11.237657191 × 1018
median1.237663278 × 1018
Q31.237668625 × 1018
95-th percentile1.237679435 × 1018
Maximum1.237680531 × 1018
Range3.465179365 × 1013
Interquartile range (IQR)1.143318292 × 1013

Descriptive statistics

Standard deviation9.234079428 × 1012
Coefficient of variation (CV)7.460892609 × 10-6
Kurtosis-0.8864431604
Mean1.237664166 × 1018
Median Absolute Deviation (MAD)6.085945065 × 1012
Skewness0.3463585308
Sum-3.414404155 × 1018
Variance8.526822288 × 1025
MonotonicityNot monotonic
2022-02-27T16:49:08.505880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.237663783 × 10183
 
< 0.1%
1.237651754 × 10182
 
< 0.1%
1.237666089 × 10182
 
< 0.1%
1.237665572 × 10182
 
< 0.1%
1.237665583 × 10182
 
< 0.1%
1.237665583 × 10182
 
< 0.1%
1.237665584 × 10182
 
< 0.1%
1.237665584 × 10182
 
< 0.1%
1.237665584 × 10182
 
< 0.1%
1.237666089 × 10182
 
< 0.1%
Other values (265461)284786
> 99.9%
ValueCountFrequency (%)
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.23764588 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
1.237645943 × 10182
< 0.1%
1.237645943 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
1.237645943 × 10181
< 0.1%
ValueCountFrequency (%)
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%

u
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct256277
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.53321096
Minimum6.137899
Maximum31.474758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2022-02-27T16:49:08.590506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.137899
5-th percentile18.845813
Q121.3885025
median22.717176
Q323.931763
95-th percentile25.6771139
Maximum31.474758
Range25.336859
Interquartile range (IQR)2.5432605

Descriptive statistics

Standard deviation2.052177283
Coefficient of variation (CV)0.09107345094
Kurtosis0.01793379229
Mean22.53321096
Median Absolute Deviation (MAD)1.261786
Skewness-0.4307088953
Sum6417616.213
Variance4.2114316
MonotonicityNot monotonic
2022-02-27T16:49:08.699882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.962665
 
< 0.1%
21.9687735
 
< 0.1%
22.1956395
 
< 0.1%
21.8308665
 
< 0.1%
22.8506685
 
< 0.1%
23.2496094
 
< 0.1%
24.4597684
 
< 0.1%
22.9734754
 
< 0.1%
22.3754
 
< 0.1%
22.3409864
 
< 0.1%
Other values (256267)284762
> 99.9%
ValueCountFrequency (%)
6.1378991
< 0.1%
7.6844861
< 0.1%
7.858441
< 0.1%
8.1079041
< 0.1%
8.1748131
< 0.1%
8.2236691
< 0.1%
9.0463741
< 0.1%
9.4453431
< 0.1%
9.5993571
< 0.1%
9.6802211
< 0.1%
ValueCountFrequency (%)
31.4747581
< 0.1%
30.6697851
< 0.1%
30.0455911
< 0.1%
30.0295751
< 0.1%
29.9149651
< 0.1%
29.5847641
< 0.1%
29.4974861
< 0.1%
29.228761
< 0.1%
29.1903691
< 0.1%
28.9785141
< 0.1%

g
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct253305
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.00671317
Minimum7.446142
Maximum32.311321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2022-02-27T16:49:08.793632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7.446142
5-th percentile17.3897861
Q119.9706715
median21.568672
Q322.33006
95-th percentile23.342819
Maximum32.311321
Range24.865179
Interquartile range (IQR)2.3593885

Descriptive statistics

Standard deviation1.90592756
Coefficient of variation (CV)0.09072945133
Kurtosis0.1467433537
Mean21.00671317
Median Absolute Deviation (MAD)0.971749
Skewness-0.8255987356
Sum5982858.957
Variance3.632559864
MonotonicityNot monotonic
2022-02-27T16:49:08.871757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.4179255
 
< 0.1%
21.643375
 
< 0.1%
21.8201375
 
< 0.1%
21.6922045
 
< 0.1%
21.8345515
 
< 0.1%
21.3907495
 
< 0.1%
22.3886625
 
< 0.1%
22.2052765
 
< 0.1%
22.5611325
 
< 0.1%
22.04595
 
< 0.1%
Other values (253295)284757
> 99.9%
ValueCountFrequency (%)
7.4461421
< 0.1%
8.2411271
< 0.1%
8.6855511
< 0.1%
8.8542821
< 0.1%
8.8799681
< 0.1%
9.0436551
< 0.1%
9.5410521
< 0.1%
9.9554121
< 0.1%
10.049981
< 0.1%
10.2044561
< 0.1%
ValueCountFrequency (%)
32.3113211
< 0.1%
32.1803591
< 0.1%
32.1802181
< 0.1%
31.121991
< 0.1%
31.0367241
< 0.1%
30.374721
< 0.1%
30.2740631
< 0.1%
30.0042611
< 0.1%
29.9160211
< 0.1%
29.6989311
< 0.1%

r
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct253330
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.78243101
Minimum8.510301
Maximum30.481144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2022-02-27T16:49:08.981132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8.510301
5-th percentile16.6154187
Q118.68987
median20.255796
Q321.016758
95-th percentile22.0144542
Maximum30.481144
Range21.970843
Interquartile range (IQR)2.326888

Descriptive statistics

Standard deviation1.724220553
Coefficient of variation (CV)0.08715918445
Kurtosis-0.04694511422
Mean19.78243101
Median Absolute Deviation (MAD)1.004759
Skewness-0.7508721697
Sum5634174.828
Variance2.972936515
MonotonicityNot monotonic
2022-02-27T16:49:09.069221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.3979436
 
< 0.1%
20.1583376
 
< 0.1%
20.4314696
 
< 0.1%
20.877316
 
< 0.1%
20.497666
 
< 0.1%
21.4367125
 
< 0.1%
20.9072825
 
< 0.1%
20.0838575
 
< 0.1%
20.9860865
 
< 0.1%
21.2219095
 
< 0.1%
Other values (253320)284752
> 99.9%
ValueCountFrequency (%)
8.5103011
< 0.1%
8.8714521
< 0.1%
9.3814331
< 0.1%
9.5374031
< 0.1%
9.544841
< 0.1%
9.8061571
< 0.1%
9.8710261
< 0.1%
10.185641
< 0.1%
10.6857151
< 0.1%
10.7132711
< 0.1%
ValueCountFrequency (%)
30.4811441
< 0.1%
27.929351
< 0.1%
27.5865121
< 0.1%
27.5316031
< 0.1%
27.4438321
< 0.1%
27.3402421
< 0.1%
26.7601411
< 0.1%
26.3037871
< 0.1%
26.2987371
< 0.1%
26.2759911
< 0.1%

i
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct253727
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.21205481
Minimum9.260902
Maximum32.286316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2022-02-27T16:49:09.157449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9.260902
5-th percentile16.225149
Q118.150341
median19.490479
Q320.358361
95-th percentile21.7069342
Maximum32.286316
Range23.025414
Interquartile range (IQR)2.20802

Descriptive statistics

Standard deviation1.700399292
Coefficient of variation (CV)0.08850689367
Kurtosis0.1441953057
Mean19.21205481
Median Absolute Deviation (MAD)1.042099
Skewness-0.4322199542
Sum5471727.693
Variance2.891357752
MonotonicityNot monotonic
2022-02-27T16:49:09.251199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.5043456
 
< 0.1%
19.6859615
 
< 0.1%
20.0969625
 
< 0.1%
19.9620575
 
< 0.1%
19.9356775
 
< 0.1%
20.1495135
 
< 0.1%
19.9589295
 
< 0.1%
20.8727575
 
< 0.1%
19.2633065
 
< 0.1%
20.2855325
 
< 0.1%
Other values (253717)284756
> 99.9%
ValueCountFrequency (%)
9.2609021
< 0.1%
9.454091
< 0.1%
9.4813671
< 0.1%
9.7858241
< 0.1%
10.004291
< 0.1%
10.2100691
< 0.1%
10.4990231
< 0.1%
10.6018751
< 0.1%
10.6670531
< 0.1%
11.0984521
< 0.1%
ValueCountFrequency (%)
32.2863161
< 0.1%
31.5754361
< 0.1%
31.3508721
< 0.1%
31.2541241
< 0.1%
31.1561531
< 0.1%
31.130891
< 0.1%
31.0734921
< 0.1%
30.8731121
< 0.1%
30.7200781
< 0.1%
30.7095891
< 0.1%

z
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct253767
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.88745681
Minimum9.688597
Maximum29.146568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2022-02-27T16:49:09.329312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9.688597
5-th percentile15.9315499
Q117.8106385
median19.084158
Q319.972534
95-th percentile21.5791392
Maximum29.146568
Range19.457971
Interquartile range (IQR)2.1618955

Descriptive statistics

Standard deviation1.724723968
Coefficient of variation (CV)0.09131583912
Kurtosis0.1293595211
Mean18.88745681
Median Absolute Deviation (MAD)1.044447
Skewness-0.2580487792
Sum5379279.912
Variance2.974672765
MonotonicityNot monotonic
2022-02-27T16:49:09.438687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.3487786
 
< 0.1%
19.1961156
 
< 0.1%
19.2320065
 
< 0.1%
19.4911145
 
< 0.1%
20.1773595
 
< 0.1%
19.296935
 
< 0.1%
19.5186965
 
< 0.1%
19.53665
 
< 0.1%
19.379985
 
< 0.1%
19.7440095
 
< 0.1%
Other values (253757)284755
> 99.9%
ValueCountFrequency (%)
9.6885971
< 0.1%
10.1116691
< 0.1%
10.1381
< 0.1%
10.2461931
< 0.1%
10.443391
< 0.1%
10.6679351
< 0.1%
10.6775681
< 0.1%
10.7370981
< 0.1%
10.839861
< 0.1%
10.8814441
< 0.1%
ValueCountFrequency (%)
29.1465681
< 0.1%
29.1050491
< 0.1%
28.9548611
< 0.1%
28.8603271
< 0.1%
28.7417531
< 0.1%
28.713531
< 0.1%
28.688851
< 0.1%
28.6705991
< 0.1%
28.6201741
< 0.1%
28.6153451
< 0.1%

uErr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct232977
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5054286479
Minimum0.011919
Maximum973.115381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2022-02-27T16:49:09.609993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.011919
5-th percentile0.041825
Q10.1732585
median0.423588
Q30.7323425
95-th percentile1.2197801
Maximum973.115381
Range973.103462
Interquartile range (IQR)0.559084

Descriptive statistics

Standard deviation1.924641703
Coefficient of variation (CV)3.80793948
Kurtosis229236.5859
Mean0.5054286479
Median Absolute Deviation (MAD)0.275584
Skewness456.7235232
Sum143949.6169
Variance3.704245683
MonotonicityNot monotonic
2022-02-27T16:49:09.691172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0940359
 
< 0.1%
0.7413547
 
< 0.1%
0.3112946
 
< 0.1%
0.0633066
 
< 0.1%
0.333226
 
< 0.1%
0.0985496
 
< 0.1%
0.046596
 
< 0.1%
0.0441896
 
< 0.1%
0.0712166
 
< 0.1%
0.6527076
 
< 0.1%
Other values (232967)284743
> 99.9%
ValueCountFrequency (%)
0.0119191
< 0.1%
0.0122591
< 0.1%
0.0126042
< 0.1%
0.0130491
< 0.1%
0.0132491
< 0.1%
0.0135271
< 0.1%
0.0136171
< 0.1%
0.0136561
< 0.1%
0.0137251
< 0.1%
0.013781
< 0.1%
ValueCountFrequency (%)
973.1153811
< 0.1%
163.3046491
< 0.1%
115.0031151
< 0.1%
86.1842531
< 0.1%
70.9041981
< 0.1%
61.4911781
< 0.1%
53.8911291
< 0.1%
40.4335751
< 0.1%
22.0307311
< 0.1%
17.7753921
< 0.1%

gErr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct171592
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1664961825
Minimum0.021987
Maximum708.703847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2022-02-27T16:49:09.784912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.021987
5-th percentile0.030867
Q10.055475
median0.124927
Q30.2062105
95-th percentile0.4196436
Maximum708.703847
Range708.68186
Interquartile range (IQR)0.1507355

Descriptive statistics

Standard deviation1.454609655
Coefficient of variation (CV)8.736594633
Kurtosis199808.3339
Mean0.1664961825
Median Absolute Deviation (MAD)0.073422
Skewness422.3944341
Sum47419.27826
Variance2.115889248
MonotonicityNot monotonic
2022-02-27T16:49:09.878672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03783713
 
< 0.1%
0.03310813
 
< 0.1%
0.0324212
 
< 0.1%
0.02951812
 
< 0.1%
0.03538811
 
< 0.1%
0.03832611
 
< 0.1%
0.03037411
 
< 0.1%
0.03315711
 
< 0.1%
0.03304311
 
< 0.1%
0.03817211
 
< 0.1%
Other values (171582)284691
> 99.9%
ValueCountFrequency (%)
0.0219871
< 0.1%
0.0221271
< 0.1%
0.0224391
< 0.1%
0.0224881
< 0.1%
0.0225521
< 0.1%
0.0225941
< 0.1%
0.0225991
< 0.1%
0.0226541
< 0.1%
0.0226951
< 0.1%
0.0226971
< 0.1%
ValueCountFrequency (%)
708.7038471
< 0.1%
220.5674561
< 0.1%
121.8288361
< 0.1%
90.4261921
< 0.1%
83.8552851
< 0.1%
77.9031991
< 0.1%
43.2650031
< 0.1%
37.2090661
< 0.1%
36.0913221
< 0.1%
32.9013231
< 0.1%

rErr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct143132
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1194891949
Minimum0.034156
Maximum39.832404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2022-02-27T16:49:09.972422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.034156
5-th percentile0.046127
Q10.063903
median0.10137
Q30.1493905
95-th percentile0.2580872
Maximum39.832404
Range39.798248
Interquartile range (IQR)0.0854875

Descriptive statistics

Standard deviation0.1375152589
Coefficient of variation (CV)1.150859365
Kurtosis28090.18909
Mean0.1194891949
Median Absolute Deviation (MAD)0.040607
Skewness118.8801239
Sum34031.35913
Variance0.01891044644
MonotonicityNot monotonic
2022-02-27T16:49:10.066172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05104413
 
< 0.1%
0.06090812
 
< 0.1%
0.0527912
 
< 0.1%
0.04416612
 
< 0.1%
0.05527612
 
< 0.1%
0.05623212
 
< 0.1%
0.05093611
 
< 0.1%
0.05352911
 
< 0.1%
0.05686811
 
< 0.1%
0.05061411
 
< 0.1%
Other values (143122)284690
> 99.9%
ValueCountFrequency (%)
0.0341561
< 0.1%
0.0344761
< 0.1%
0.0346441
< 0.1%
0.0346971
< 0.1%
0.0349011
< 0.1%
0.0350191
< 0.1%
0.0351191
< 0.1%
0.0352071
< 0.1%
0.0352651
< 0.1%
0.0352891
< 0.1%
ValueCountFrequency (%)
39.8324041
< 0.1%
22.3278851
< 0.1%
12.8581851
< 0.1%
12.2722541
< 0.1%
12.1865371
< 0.1%
10.613931
< 0.1%
9.5024491
< 0.1%
8.9521561
< 0.1%
8.8973921
< 0.1%
8.3730051
< 0.1%

iErr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct136364
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1321145217
Minimum0.033318
Maximum66.143307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2022-02-27T16:49:10.163644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.033318
5-th percentile0.0578893
Q10.0761455
median0.101908
Q30.143657
95-th percentile0.2997737
Maximum66.143307
Range66.109989
Interquartile range (IQR)0.0675115

Descriptive statistics

Standard deviation0.2091837423
Coefficient of variation (CV)1.583351623
Kurtosis38116.37042
Mean0.1321145217
Median Absolute Deviation (MAD)0.030354
Skewness142.2519051
Sum37627.14057
Variance0.04375783806
MonotonicityNot monotonic
2022-02-27T16:49:10.257394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07677513
 
< 0.1%
0.06852413
 
< 0.1%
0.07562112
 
< 0.1%
0.07640212
 
< 0.1%
0.07013912
 
< 0.1%
0.14178412
 
< 0.1%
0.07518911
 
< 0.1%
0.09062111
 
< 0.1%
0.08371511
 
< 0.1%
0.0672411
 
< 0.1%
Other values (136354)284689
> 99.9%
ValueCountFrequency (%)
0.0333182
< 0.1%
0.0395561
< 0.1%
0.0395691
< 0.1%
0.040541
< 0.1%
0.0414321
< 0.1%
0.0414431
< 0.1%
0.0415041
< 0.1%
0.0417341
< 0.1%
0.0417521
< 0.1%
0.0420231
< 0.1%
ValueCountFrequency (%)
66.1433071
< 0.1%
32.416251
< 0.1%
20.4407171
< 0.1%
20.0244661
< 0.1%
18.3347891
< 0.1%
17.4917231
< 0.1%
13.2475611
< 0.1%
11.9594821
< 0.1%
11.8463591
< 0.1%
11.371971
< 0.1%

zErr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct171161
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2198274226
Minimum0.044069
Maximum47.529248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2022-02-27T16:49:10.351144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.044069
5-th percentile0.0796933
Q10.110915
median0.151384
Q30.229893
95-th percentile0.628709
Maximum47.529248
Range47.485179
Interquartile range (IQR)0.118978

Descriptive statistics

Standard deviation0.2839616411
Coefficient of variation (CV)1.291748035
Kurtosis4520.541349
Mean0.2198274226
Median Absolute Deviation (MAD)0.049726
Skewness41.65436631
Sum62608.38874
Variance0.08063421359
MonotonicityNot monotonic
2022-02-27T16:49:10.444894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.14603910
 
< 0.1%
0.14338110
 
< 0.1%
0.12868810
 
< 0.1%
0.12354710
 
< 0.1%
0.106859
 
< 0.1%
0.1086539
 
< 0.1%
0.1057649
 
< 0.1%
0.1166649
 
< 0.1%
0.1152169
 
< 0.1%
0.1067539
 
< 0.1%
Other values (171151)284713
> 99.9%
ValueCountFrequency (%)
0.0440691
< 0.1%
0.0441532
< 0.1%
0.0442971
< 0.1%
0.0446671
< 0.1%
0.0450211
< 0.1%
0.0454211
< 0.1%
0.0467181
< 0.1%
0.0470861
< 0.1%
0.047521
< 0.1%
0.0476191
< 0.1%
ValueCountFrequency (%)
47.5292481
< 0.1%
29.8753021
< 0.1%
28.7136821
< 0.1%
26.3650851
< 0.1%
23.7111791
< 0.1%
22.2426741
< 0.1%
19.0154721
< 0.1%
17.9289941
< 0.1%
17.2466541
< 0.1%
16.9436931
< 0.1%

Interactions

2022-02-27T16:49:05.845123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:47.368378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:49.258337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:51.134241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:52.983564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:54.762048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:56.618721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:58.541914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:00.297349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:02.156672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:04.009953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:06.001739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:47.536699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:49.436768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:51.298740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:53.159553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:54.945871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:56.801575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:58.707832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:00.466086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:02.320393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:04.176992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:06.167499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:47.752743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:49.592839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:51.467261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:53.308119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:55.097020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:56.969798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:58.861121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:00.633139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:02.489485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:04.329605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:06.317794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:47.922790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:49.761112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:51.619478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:53.473429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:55.265037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:57.137088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:59.026021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:00.861446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:02.639459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:04.494668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:06.470740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:48.087559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:49.912544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:51.783811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:53.639416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:55.414383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:57.289246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:59.179227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:01.017236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:02.809488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:04.646443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:06.619915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:48.255270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:50.078996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:51.947999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:53.792862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:55.643566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:57.455424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:59.329198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:01.169636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:02.957811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:04.817667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:06.788108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:48.424001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:50.309096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:52.115270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:53.958034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:55.800957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:57.637764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:59.505773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:01.336091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:03.124959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:04.979714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:06.952750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:48.589306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:50.480508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:52.268861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:54.111357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:55.966373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:57.806283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:59.661205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:01.507602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:03.354520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:05.131818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:07.105667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:48.764304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:50.632266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:52.436467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:54.278400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:56.132599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:57.985710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:59.814442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:01.668375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:03.510521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:05.297518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:07.271123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:48.932559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:50.814322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:52.605047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:54.445557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:56.300261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:58.219333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:59.979660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:01.837283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:03.677822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:05.468087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:07.424192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:49.091051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:50.965545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:52.755541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:54.610321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:56.466999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:48:58.390105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:00.131700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:01.987426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:03.844819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-27T16:49:05.616679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-27T16:49:10.523019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-27T16:49:10.697667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-27T16:49:10.807042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-27T16:49:10.916417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-27T16:49:07.548633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-27T16:49:07.771692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IDugrizuErrgErrrErriErrzErr
0123764587956292880525.15537522.23298121.25784119.88985419.4271071.0346570.2190330.2069520.1473470.228686
1123764594290550404021.91066719.46443918.37297817.95550217.6429790.4438520.0606660.0648090.0699810.096798
2123764594290563518020.97867020.27682119.47230519.23270618.9941770.1501200.0708100.0768910.0855700.132459
3123764594290622486020.38998019.31364317.89161517.41506617.1812310.3316360.1132950.0923970.0931780.127449
4123764594397872152023.90716923.44867520.25651719.16386018.5571521.5279601.0334150.1774880.1328690.149485
5123764594397878713820.50301419.28054218.83047718.55928018.4818270.1202140.0447400.0615190.0719900.098771
6123764594397878721221.72803519.97735818.90186518.37909918.0371950.3158950.0619510.0635940.0686380.084922
7123764594397878723820.09530118.80882518.19224417.88397017.6554570.1393420.0546250.0730510.0855040.116468
8123764594397885252619.98365218.80582817.91803917.49641017.2386510.0928220.0422390.0540850.0625370.077156
9123764594397885286524.60208721.48808719.52091018.92142518.5942520.8667160.1796410.0909750.0914850.116313

Last rows

IDugrizuErrgErrrErriErrzErr
284797123767944042558296523.63934722.19899921.03185320.62351620.2202761.1643130.2077200.1689910.1897670.281953
284798123767944042558299325.01959823.56868221.38697220.71624620.1403731.1668850.5109550.1810360.1706060.219201
284799123767944042558299923.80009122.66311621.37600521.03338420.5094280.7580780.1726920.1219770.1411940.193995
284800123767944042558306524.12803622.61571922.08888422.17021822.7805810.8401290.1623390.1931050.2956700.616266
284801123767944042558332124.79755024.09209122.12824821.59173221.1697860.8273330.4694210.2019030.1988410.295564
284802123767944042558332924.97644824.60020122.03163921.27790621.7423590.9938660.7457570.2372570.2035700.556340
284803123767944042558335222.11512622.93945321.89254621.64350921.1614910.2979240.2879820.2382970.2872310.418816
284804123767944042558339325.05322323.42927221.97012121.79635421.7643320.9601380.3748650.2233370.2893260.562293
284805123767944042558340623.51894823.86224722.47657023.32341222.4735530.6650530.4265650.2710130.6746570.628833
284806123767944042558355223.85055523.66226822.62598421.67310320.7279070.9956840.4756500.3804950.2794380.296898

Duplicate rows

Most frequently occurring

IDugrizuErrgErrrErriErrzErr# duplicates
10069123766378259382280522.28327421.06804820.49555220.15277320.3204460.2801620.0975350.1123810.1284070.2694803
0123764594290504529221.54909120.44907219.49150519.10948219.0315700.1877750.0643910.0707610.0782790.1330992
1123764594290576635022.38147219.82943219.19526718.88984718.7831060.5652670.0653010.0799790.0876330.1430012
2123764594290602854321.95227819.57007619.22419719.06801819.5869120.7794890.1043540.1271840.1398300.2965082
3123764594290629077521.61922321.16918419.88048919.31565919.0672360.5797380.2482830.1621710.1334100.1937692
4123764594343352775922.58504922.08953920.59684620.02345119.6932750.2513150.1317260.0851360.0902150.1255462
5123764594343352815425.50357623.37670921.65586521.13076820.6118110.4154020.3372250.1559600.1614060.2295322
6123764594397649333621.05897920.26852819.70432119.55120719.3454760.1396710.0592610.0753140.0936540.1331402
7123764594397859044823.60477320.86562719.52021618.99168818.6033120.8556190.0860420.0710800.0754680.0924402
8123764594397885285123.21190621.69883920.06878519.53569019.1617980.8421520.1825760.1048620.1059670.1417062